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(SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI methods for such searches
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group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification in large pre-trained models (vision-language models), generative models (flow
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